In recent years, the world has experienced an explosive growth in the popularity of mobile computing devices, such as mobile phones, tablets, and other form-factor devices, such as smart watches, smart glasses, and the like. These devices tend to use touch sensing as one of their primary user-input mechanisms. Touch-sensitive sensors will generally measure the location of taps, swipes, and other gestures produced by the user.
Detecting finger gestures accurately is of utmost importance to device designers since a user's perception of the quality of a device is closely bound to the user's personal use experience. Finger gesture recognition also presents some critical challenges for mobile devices since users tend to have different physical characteristics and habitual behavior. For example, users may have different skin textures, affix a wearable device differently, apply a gesture with different degrees of force, or tap the device in different patterns. These kinds of variations complicate the design of mobile devices for accurate gesture recognition.
Presently, touch-sensitive devices offer a calibration, or optimization algorithm to tune their responsiveness to user-applied touch gestures for specific users. While a fully-tuned touch gesture detection algorithm may accommodate the majority, say 90%, of the user population reasonably well, there remains a small but very vocal group of users who struggle with incorrectly-interpreted gestures. Properly addressing the user experience issues, particularly from a disgruntled group, becomes a high priority in managing the brand image for a wearable device.